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Winter Wheat Yield Prediction Using an LSTM Model from MODIS LAI Products

Author

Listed:
  • Jian Wang

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Haiping Si

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Zhao Gao

    (The First Geodetic Survey Team of the Ministry of Natural Resources, Shaanxi Bureau of Surveying, Mapping and Geoinformation, Xi’an 710054, China)

  • Lei Shi

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

Abstract

Yield estimation using remote sensing data is a research priority in modern agriculture. The rapid and accurate estimation of winter wheat yields over large areas is an important prerequisite for food security policy formulation and implementation. In most county-level yield estimation processes, multiple input data are used for yield prediction as much as possible, however, in some regions, data are more difficult to obtain, so we used the single-leaf area index (LAI) as input data for the model for yield prediction. In this study, the effects of different time steps as well as the LAI time series on the estimation results were analyzed for the properties of long short-term memory (LSTM), and multiple machine learning methods were compared with yield estimation models constructed by the LSTM networks. The results show that the accuracy of the yield estimation results using LSTM did not show an increasing trend with the increasing step size and data volume, while the yield estimation results of the LSTM were generally better than those of conventional machine learning methods, with the best R 2 and RMSE results of 0.87 and 522.3 kg/ha, respectively, in the comparison between predicted and actual yields. Although the use of LAI as a single input factor may cause yield uncertainty in some extreme years, it is a reliable and promising method for improving the yield estimation, which has important implications for crop yield forecasting, agricultural disaster monitoring, food trade policy, and food security early warning.

Suggested Citation

  • Jian Wang & Haiping Si & Zhao Gao & Lei Shi, 2022. "Winter Wheat Yield Prediction Using an LSTM Model from MODIS LAI Products," Agriculture, MDPI, vol. 12(10), pages 1-13, October.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1707-:d:944103
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    References listed on IDEAS

    as
    1. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    2. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
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    Cited by:

    1. Jibo Yue & Chengquan Zhou & Haikuan Feng & Yanjun Yang & Ning Zhang, 2023. "Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring," Agriculture, MDPI, vol. 13(10), pages 1-4, October.
    2. Juan D. Borrero & Jesús Mariscal & Alfonso Vargas-Sánchez, 2022. "A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors," Stats, MDPI, vol. 5(4), pages 1-14, November.
    3. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2023. "Prediction of Pea ( Pisum sativum L.) Seeds Yield Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
    4. Shanxin Zhang & Hao Feng & Shaoyu Han & Zhengkai Shi & Haoran Xu & Yang Liu & Haikuan Feng & Chengquan Zhou & Jibo Yue, 2022. "Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    5. Sebastian C. Ibañez & Christopher P. Monterola, 2023. "A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers," Agriculture, MDPI, vol. 13(9), pages 1-27, September.

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